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The development of Road Information Acquisition Systems (RIASs) based on the Mobile Crowdsensing (MCS) paradigm has been widely studied for the last years. In that sense, most of the existing MCS-based RIASs focus on urban road networks and assume a car-based scenario. However, there exist a scarcity of approaches that pay attention to rural and co...
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... this section, we describe in detail the SAMAR-ITAN framework. In that sense, Fig. 2 shows its key steps. As we can see, SAMARITAN follows a crowdsensing approach where steps 2, 3 and 4 are executed in the contributors' devices whereas step 5 is executed in a backend server. The following subsections describe in detail each of these ...
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... first stage of the framework pipeline focuses on collecting the individual spatio-temporal trajectories of people moving around the target area of interest as Fig. 2 shows. For that goal, we make use of the Application Programming Interface (API) of the Strava ...
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... the trajectories collected from a particular cyclist c conform the set T R c . In that sense, as Fig. 2 shows, the SAMARITAN framework performs a set of analytical steps over each set of trajectories T R c for all the target cyclists C. These steps are labelled as 2a, 2b, 3 and 4 in the ...
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... from the segmentation described above, we also analyze each incoming raw trajectory tr c so as to detect the parts where the cyclist c moved quite slowly (step 2b in Fig. ...
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... this step of SAMARITAN takes as input the Strava segments tr c S and the set of low-speed points tr c ls of each incoming trajectory tr c (see Fig. 2). Algorithm 1 summarizes the mapping processing perform at this stage of the ...
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... Fig. 2 depicts, we should mention that the four procedures described in Sections 3.2 to 3.5 are executed independently for each cyclist in ...
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... do so, SAMARITAN follows a batch-based analysis (depicted as step 5a in Fig. 2). To begin with, it defines a time-based sliding window that collects all the sets tr c ISLSP generated by all cyclists C during the last ISLSP hours. The content of this sliding-windows is defined as the set W ISLSP . This set contains all the ISLSPs occurred in the most recent trajectories received by ...
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... the sake of clarity, here we sum up the key steps that compose the processing pipeline of the SAMARI-TAN framework as shown in Fig. ...
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... of all, the client-side SAMARITAN collects the spatio-temporal trajectories of its target cyclist moving around the spatial area under monitoring (step 1 in Fig. 2). Then, these trajectories are split based on the community-defined Strava segments and their low-speed points are uncovered (steps 2a and 2b). Next, these low-speed points are mapped based on the Strava segments (step 3). This gives raise to OSLSPs and ISLSPs. After that, the OSLSP are clustered so as to uncover the r-OSLSPs (step ...
Citations
... The advent of the Internet of Things (IoT) has come with the development of several indoor location solutions based on different wireless technologies like WiFi [37], RFID [3,18] or Bluetooth Low Energy (BLE) [17]. One of the most prominent applications of these location technologies has been the development of location-based services (LBSs) able to adapt to users' locations inside buildings [6], provide users with ambient intelligence [19] or detect incidents in road infrastructures [4]. In this context, several proposals have recently arisen in the cultural environment in order to profit from such IoT advances. ...
The Internet of Things (IoT) has recently been applied in the domain of cultural exhibition enabling the cultural sites to provide more personal and proactive experiences to their visitors. To come up with valuable services, several solutions to analyze the spatio-temporal trajectories of visitors have been put forward. However, they neither consider the inherent uncertainty of the underlying indoor positioning technologies – Bluetooth Low Energy (BLE), RFID, etc. – nor other visitors’ features apart from the spatio-temporal ones (e.g. the level of interaction with the museum displays). For that reason, the present work introduces RECITE, a framework to classify trajectories representing visitors’ actions that copes with the aforementioned limitations of existing solutions. Firstly, RECITE states a novel mapping process for a BLE-based indoor positioning system to accurately detect the visitors’ locations. On top of this mechanism, RECITE includes an ensemble of fuzzy rule classifiers able to tag the visitors’ ongoing trajectories in real time considering both spatio-temporal and other behavioural factors. Finally, the framework has been evaluated in a case of use scenario showing quite promising results.
